Significant difficulties are experienced by users when programmable complex devices are infrequently used or programmed, or when a user attempts to use uncommon functions of these devices, such as, for example video cassette recorders (hereinafter “VCRs”). Studies have concluded that 80% of users cannot correctly program their VCRs. This has been due, in part, to the fact that manufacturers continue to add more features to existing devices, without simplifying those which already exist.
People learn most efficiently through the interactive experiences of doing, thinking, and knowing. For ease-of-use, efficiency, and lack of frustration of the user, utilizing the device should be intuitive. Users should be able to operate the device without referring to an instruction manual. Well-designed products should contain visual clues which prompt and convey their meanings, however, prior art devices do not always live up to this ideal. This problem is accentuated by various manufacturers and designers who focus on the production and design of feature-rich systems, rather than on ones which are also “User Friendly” and thus easier to use. Therefore, many products are extremely complex and thus difficult to use, thereby preventing all but the most technically advanced people from using them.
The act of programming, or determining a sequence of operations to be performed by, for example, a VCR, several steps are required. In addition to setting the clock, the user must assign a program number, set the current date and current time, select the start and stop times, choose the channel from which to record, and choose a tape speed. These actions require a minimum of four actuators (“Program”, “+”, “−”, and “Enter”). Presently, some VCR controls contain up to 123 buttons, double function keys, and symbols which are not immediately recognized by the user.
In order to simplify commonly-used functions, a number of methods have been devised. Certain VCRs employ a bar-code reader in order to allow entry of programming steps from a menu of functions, or from an encoded description of an event to be programmed. However, this method suffers from the limitation that the channel, time and duration must be available in encoded form, otherwise the use of the device will not simplify the use or programming of the VCR. These machines come with a laminated sheet of bar codes. In order to program the VCR, the user must press a button on a wand, which lights its tip, and then run or pass the tip over a bar-code, to set each step separately. Finally, when all the information has been scanned in, the user must press the “Transmit” button. The “VCRplus+” is a device which allows the entry of a code representing a channel, time, date and duration of a program to be recorded, which when entered into the remote control device, is translated into commands for programming the VCR, and transmitted through an infrared link to the VCR, thus programming the VCR. This system has the limitations that the published codes must be available, and manually entered, which may be thus be erroneously entered, and the system does not allow for rescheduled programs, so that any variation in schedule will result in a defective recording. The time and date in the VCR device must also be set accurately for this system to operate.
On-screen programming systems exist; however, these generally require the user to scroll through menus and option choices without allowing direct entry of programming information. Direct-entry systems are available with, for example, programmable controllers with keypad entry. However, these do not generally have full information visual displays, meaning that all vital information is not or cannot be simultaneously displayed, and must be “multiplexed”, meaning that data must share display space with other data, displayed at different times. In a VCR with On-screen programming, all programming information feedback is displayed on the television screen, and prompts are provided to guide the user through the necessary steps. Some VCRs have numeric keypads to enter the information, while others allow choices to be entered by the selection method, which depends on the use of “up” and “down” arrow keys to select a desired option.
The other major presently used method, which is available on most VCRs, as well as other types of programmable devices, is Display Panel Programming. This method is generally inadequate because full instructions are not normally available on the display panel, and the amount of information simultaneously displayed is limited. Users do not need a television set to see the displayed information, but they might have trouble reading the small, usually multifunctional multiplexed display and keypad. When programming the VCR, information may be entered on the display panel using the selection method, with either the “up” key or both “up” and “down” keys, or by direct entry in devices that support such a system.
The remote control device of a VCR is often the primary input device, and it sometimes has control functions not accessible from a keypad input present on the VCR itself. Remote controls often contain many buttons, which may be found overwhelming and confusing by the user. This results in under-utilization of the various actuators or buttons, and consequently, various useful features are unused or inaccessible, or the programming operation is inefficient. The extra clutter results in a greater “search time”, the time needed to locate and execute a desired function, and thus it takes longer to program the VCR. The general structure of the search time in programming a VCR is shown diagrammatically in FIG. 1. Other problems arise from the layout and coding of the buttons. A study performed by Kamran Abedini and George Hadad in 1987 entitled “Guidelines for Designing Better VCRs”, Report No. IME 462, Feb. 4, 1987, California State Polytechnic University, incorporated herein by reference, has shown that varying the shape of the remote control device is more effective than varying its size. In addition, they found that color coding and adequate contrast can effect a significant improvement in programming performance. Abedini and Kamran, in “An Ergonomically-improved Remote Control Unit Design”, Interface '87 Proceedings, 375–380 (1987), incorporated herein by reference, found that 78% of the people surveyed favored direct entry numbers (0–9) in addition to labels, symbols, discrete volume switches, and channel up/down buttons for casual searching. In addition, the people surveyed preferred remote controls which fit comfortably into their hand.
Many techniques have been used to facilitate the programming of devices such as VCRs, including:                Display Panels (1982)—Programmed with the aid of an LED display panel on the front of the machine.        Programming Via Remote Control (1983)—Programmed using a remote control device with keys for input.        On-Screen Displays (1984)—Programmed by a series of menus on the television screen.        Bar Code Scanners (1987)—Programmed by a wand passing over a series of lines, which are decoded and then transmitted to the VCR.        Light Pens (1987)—Programmed by aiming a pointing device with a light beam sensor at the television screen, which allows timing signals to be extracted to determine the position of the device with respect to the screen, and hence, the intended instruction.        Video Program System Signal Transmitters (1988)—The VCR is programmed by entering the unique code number of a desired program to record, which is emitted by television stations in West Germany as videotext digital signals associated with each program.        Phone Lines (1989)—Programmed over a telephone line at from a remote location. The numeric keys on the phone are the input keys.        Video Memories (1989)—Programmed by a computer from a remote location. For example, a user contacts a service, who then records certain programs at a user's request. These can be characterized in a number of ways, e.g. comedies, movies, etc. and the service will then manually scan the broadcast schedules for these provided characterizations and record the desired programs.        Voice Coaches (1990)—Programmed by responding to, voice instructions, e.g. speech prompts, from the remote control.        
As the technology becomes more mature, and VCRs and other types of programmable consumer electronic devices become less expensive, a proportionally less-educated segment of society will be confronted with these devices. While education and ability to program a VCR are not necessarily correlated, the present invention is directed toward improving the interface to allow all segments of the population to effectively interface with these programmable devices. By making the user interface more intuitive, and facilitating program entry by all levels of users, the present method and apparatus allow a manufacturer to produce a single device, without regard to the ability of the user to learn the programming steps. It is also noted that, because of their previous inability to provide a programmable consumer electronic device with various user interface levels, manufacturers have had to compromise the programming power of their user interface to allow less than advanced users to program it, or to compromise the usability of the device in order to make the full programming power available.
Technology for Implementing the Human Interface, Image Processing and Decision Making Methods of the Present Invention
The following references are relevant to the interface aspects of the present invention, are contained in the appendix hereto, and are expressly incorporated herein by reference:    Hoffberg, Linda I, “AN IMPROVED HUMAN FACTORED INTERFACE FOR PROGRAMMABLE DEVICES: A CASE STUDY OF THE VCR” Master's Thesis, Tufts University (Master of Sciences in Engineering Design, November, 1990)    “Bar Code Programs VCR”, Design News, Feb. 1, 1988, 26.    “The Highs and Lows of Nielsen Homevideo Index”, Marketing & Media Decisions, November 1985, 84–86+.    “The Quest for ‘User Friendly’”, U.S. News & World Report, Jun. 13, 1988. 54–56.    “The Smart House: Human Factors in Home Automation”, Human Factors in Practice, December 1990, 1–36.    “VCR, Camcorder Trends”, Television Digest, Vol. 29, Mar. 20, 1989, 16.    Abedini, Kamran, “An Ergonomically-improved Remote Control Unit Design”, Interface '87 Proceedings, 375–380.    Abedini, Kamran, and Hadad, George, “Guidelines For Designing Better VCRs”, Report No. IME 462, Feb. 4, 1987.    Bensch, U., “VPV—VIDEOTEXT PROGRAMS VIDEORECORDER”, IEEE Transactions on Consumer Electronics, Vol. 34, No. 3, 788–792.    Berger, Ivan, “Secrets of the Universals”, Video, February 1989, 45–47+.    Beringer, D. B., “A Comparative Evaluation of Calculator Watch Data Entry Technologies: Keyboards to Chalkboards”, Applied Ergonomics, December 1985, 275–278.    Bishop, Edward W., and Guinness, G. Victor Jr., “Human Factors Interaction with Industrial Design”, Human Factors, Vol. 8, No. 4, August 1966, 279–289.    Brown, Edward, “Human Factors Concepts For Management”, Proceedings of the Human Factors Society, 1973, 372–375.    Bulkeley, Debra, “The Smartest House in America”, Design News, Oct. 19, 1987, 56–61.    Card, Stuart K., “A Method for Calculating Performance times for Users of Interactive Computing Systems”, IEEE, 1979, 653–658.    Carlson, Mark A., “Design Goals for an Effective User Interface”, Electro/82 Proceedings, 3/1/1–3/1/4.    Carlson, Mark A., “Design Goals for an Effective User Interface”, Human Interfacing with Instruments, Session 3.    Carroll, Paul B., “High Tech Gear Draws Cries of “Uncle”, Wall Street Journal, Apr. 27, 1988, 29.    Cobb, Nathan, “I don't get it”, Boston Sunday Globe Magazine, Mar. 25, 1990, 23–29.    Davis, Fred, “The Great Look-and-Feel Debate”, A+, Vol. 5, July 1987, 9–11.    Dehning, Waltraud, Essig Heidrun, and Maass, Susanne, The Adaptation of Virtual Man-Computer Interfaces to User Requirements in Dialogs, Germany: Springer-Verlag, 1981.    Ehrenreich, S. L., “Computer Abbreviations—Evidence and Synthesis”, Human Factors, Vol. 27, No. 2, April 1985, 143–155.    Friedman, M. B., “An Eye Gaze Controlled Keyboard”, Proceedings of the 2nd International Conference on Rehabilitation Engineering, 1984, 446–447.    Gilfoil, D., and Mauro, C. L., “Integrating Human Factors and Design: Matching Human Factors Methods up to Product Development”, C. L. Mauro Assoc., Inc., 1–7.    Gould, John D., Boies, Stephen J., Meluson, Antonia, Rasammy, Marwan, and Vosburgh, Ann Marie, “Entry and Selection Methods For Specifying Dates”. Human Factors, Vol. 32, No. 2, April 1989, 199–214.    Green, Lee, “Thermo Tech: Here's a common sense guide to the new thinking thermostats”, Popular Mechanics, October 1985, 155–159.    Grudin, Jonathan, “The Case Against User Interface Consistency”, MCC Technical Report Number ACA-HI-002-89, January 1989.    Harvey, Michael G., and Rothe, James T., “VideoCassette Recorders: Their Impact on Viewers and Advertisers”, Journal of Advertising, Vol. 25, December/January 1985, 19–29.    Hawkins, William J., “Super Remotes”, Popular Science, February 1989, 76–77.    Henke, Lucy L., and Donohue, Thomas R., “Functional Displacement of Traditional TV Viewing by VCR Owners”, Journal of Advertising Research, V29, April–May 1989, 18–24.    Hoban, Phoebe, “Stacking the Decks”, New York, Feb. 16, 1987, Vol. 20, 14.    “How to find the best value in VCRs”, Consumer Reports, March 1988, 135–141.    Howard, Bill, “Point and Shoot Devices”, PC Magazine, Vol 6, August 1987, 95–97.    Jane Pauley Special, NBC TV News Transcript, Jul. 17, 1990, 10:00 PM.    Kolson, Ann, “Computer wimps drown in a raging sea of technology”, The Hartford Courant, May 24, 1989, B1.    Kreifeldt, J. G., “A Methodology For Consumer Product Safety Analysis”, The 3rd National Symposium on Human Factors in Industrial Design in Consumer Products, August 1982, 175–184.    Kreifeldt, John, “Human Factors Approach to Medical Instrument Design”. Electro/82 Proceedings, 3/3/1–3/3/6.    Kuocheng, Andy Poing, and Ellingstad, Vernon S., “Touch Tablet and Touch Input”, Interface '87, 327.    Ledgard, Henry, Singer, Andrew, and Whiteside, John, Directions in Human Factors for Interactive Systems, New York: Springer-Verlag, 1981.    Lee, Eric, and MacGregor, James, “Minimizing User Search Time Menu Retrieval Systems”, Human Factors, Vol. 27, No. 2, April 1986, 157–162.    Leon, Carol Boyd, “Selling Through the VCR”, American Demographics, December 1987, 40–43.    Long, John, “The Effect of Display Format on the Direct Entry of Numerical Information by Pointing”, Human Factors, Vol. 26, No. 1, February 1984, 3–17.    “Low-Cost VCRs: More For Less”, Consumer Reports, March 1990, 168–172.    Mantei, Marilyn M., and Teorey, Toby J., “Cost/Benefit Analysis for Incorporating Human Factors in the Software Lifecycle”, Association for Computing Machinery, 1988.    Meads, Jon A., “Friendly or Frivolous”, Datamation, Apr. 1, 1988, 98–100.    Moore, T. G. and Dartnall, “Human Factors of a Microelectronic Product: The Central Heating Timer/Programmer”, Applied Ergonomics, 1983, Vol. 13, No. 1, 15–23.    “Nielsen Views VCRs”, Television Digest, Jun. 23, 1988, 15.    Norman, Donald A., “Infuriating By Design”, Psychology Today, Vol. 22, No. 3, March 1988, 52–56.    Norman, Donald A., The Psychology of Everyday Things, New York: Basic Book, Inc. 1988.    Platte, Hans-Joachim, Oberjatzas, Gunter, and Voessing, Walter, “A New Intelligent Remote Control Unit for Consumer Electronic Device”, IEEE Transactions on Consumer Electronics, Vol. CE-31, No. 1, February 1985, 59–68.    Rogus, John G. and Armstrong, Richard, “Use of Human Engineering Standards in Design”, Human Factors, Vol. 19, No. 1, February 1977, 15–23.    Rosch, Winn L., “Voice Recognition: Understanding the Master's Voice”, PC Magazine, Oct. 27, 1987, 261–308.    Sarver, Carleton, “A Perfect Friendship”, High Fidelity, Vol. 39, May 1989, 42–49.    Schmitt, Lee, “Let's Discuss Programmable Controllers”, Modern Machine Shop, May 1987, 90–99.    Schniederman, Ben, Designing the User Interface: Strategies for Effective Human-Computer Interaction, Reading, Mass.: Addison-Wesley, 1987.    Smith, Sidney J., and Mosier, Jane N., Guidelines for Designing User Interface Software, Bedford, Mass.: MITRE, 1986.    Sperling, Barbara Bied, & Tullis Thomas S., “Are You a Better ‘Mouser’ or ‘Trackballer’? A Comparison of Cursor-Positioning Performance”, An Interactive/Poster Session at the CHI+GI'87 Graphics Interface and Human Factors in Computing Systems Conference.    Streeter, L. A., Ackroff, J. M., and Taylor, G. A. “On Abbreviating Command Names”, The Bell System Technical Journal, Vol. 62, No. 6, July/August 1983, 1807–1826.    Swanson, David, and Klopfenstein, Bruce, “How to Forecast VCR Penetration”, American Demographic, December 1987, 44–45.    Tello, Ernest R., “Between Man And Machine”, Byte, September 1988, 288–293.    Thomas, John, C., and Schneider, Michael L., Human Factors in Computer Systems, New Jersey: Ablex Publ. Co., 1984.    Trachtenberg, Jeffrey A., “How do we confuse thee? Let us count the ways”, Forbes, Mar. 21, 1988, 159–160.    Tyldesley, D. A., “Employing Usability Engineering in the Development of Office Products”, The Computer Journal”, Vol. 31, No. 5, 1988, 431–436.    “VCR's: A Look At The Top Of The Line”, Consumer Reports, March 1989, 167–170.    Verplank, William L., “Graphics in Human-Computer Communication: Principles of Graphical User-Interface Design”, Xerox Office Systems.    “VHS Videocassette Recorders”, Consumer Guide, 1990, 17–20.    Voyt, Carlton F., “PLC's Learn New Languages”, Design News, Jan. 2, 1989, 78.    Whitefield, A. “Human Factors Aspects of Pointing as an Input Technique in Interactive Computer Systems”, Applied Ergonomics, June 1986, 97–104.    Wiedenbeck, Susan, Lambert, Robin, and Scholtz, Jean, “Using Protocol Analysis to Study the User Interface”, Bulletin of the American Society for Information Science, June/July 1989, 25–26.    Wilke, William, “Easy Operation of Instruments by Both Man and Machine”. Electro/82 Proceedings, 3/2/1–3/2/4.    Yoder, Stephen Kreider, “U.S. Inventors Thrive at Electronics Show”, The Wall Street Journal, Jan. 10, 1990, B1.    Zeisel, Gunter & Tomas, Philippe & Tomaszewski, Peter, “An Interactive Menu-Driven Remote Control Unit for TV-Receivers and VC-Recorders”, IEEE Transactions on Consumer Electronics, Vol. 34, No. 3, 814–818.
The following cited patents and publications are relevant to pattern recognition and control aspects of the present invention, and are herein expressly incorporated by reference:
U.S. Pat. No. 5,067,163, incorporated herein by reference, discloses a method for determining a desired image signal range from an image having a single background, in particular a radiation image such as a medical X-ray. This reference teaches basic image enhancement techniques.
U.S. Pat. No. 5,068,664, incorporated herein by reference, discloses a method and device for recognizing a target among a plurality of known targets, by using a probability based recognition system. This patent document cites a number of other references, each incorporated herein by reference, which are relevant to the problem of image recognition: Proceedings of the 1988 IEEE National Radar Conference, 20–21 Apr. 1988, pp. 157–164, Vannicola et al., “Applications of Knowledge Based Systems to Surveillance”; Ksienski et al., “Low Frequency Approach to Target Identification”, Proc. of the IEEE, vol. 63, No. 12, pp. 1651–1660, December 1975; A. Appriou, “Interet des theories de l'incertain en fusion de donnees”, Colloque International sur le Radar Paris, 24–28 avril 1989; A. Appriou “Procedure d'aide a la decision multi-informateurs. Applications a la classification multi-capteurs de cibles”, Symposium de l'Avionics Panel (AGARD) Turquie, 25–29 avril 1988; K. J. Arrow, “Social choice and individual valves”, John Wiley and Sons Inc. (1963); D. Blair, R. Pollack, “La logique du choix collectif” Pour la Science (1983); A. Scharlic, “Decider sur plusieurs criteres. Panorama de l'aide a la decision multicritere” Presses Polytechniques Romandes (1985), R. L. Keeney, B. Raiffa, “Decisions with multiple objectives: Preferences and value tradeoffs”, John Wiley and Sons, New York (1976); R. J. Jeffrey, “The logic of decision”, The University of Chicago Press, Ltd., London (1983)(2nd Ed.); B. Roy, “Classements et choix en presence de points de vue multiples”, R.I.R.O.-2eme annee-no. 8, pp. 57–75 (1968); B. Roy “Electre III: un algorithme de classements fonde sur une representation floue des preferences en presence de criteres multiples” Cahiers du CERO, Vol. 20, no. 1, pp. 3–24 (1978); R. O. Duda, P. E. Hart, M. J. Nilsson, “Subjective Bayesian methods for rule-based inference systems”, Technical Note 124-Artificial Intelligence Center-SRI International; R. K. Bhatnagar, L. N. Kamal, “Handling uncertain information: a review of numeric and non-numeric methods”, Uncertainty in Artificial Intelligence, L. N. Kamal and J. F. Lemmer, Eds. (1986); A. P. Dempster, “Upper and lower probabilities induced by a multivalued mapping”, Annals of mathematical Statistics, no. 38 (1967); A. P. Dempster, “A generalization of Bayesian inference”, Journal of the Royal Statistical Society, Vol. 30, Series B (1968); G. Shafer, “A mathematical theory of evidence”, Princeton University Press, Princeton, N.J. (1976); D. Dubois, N. Prade, “Combination of uncertainty with belief functions: a reexamination”, Proceedings 9th International Joint Conference on Artificial Intelligence, Los Angeles (1985); H. E. Kyburg, “Bayesian and non Bayesian evidential updating”, Artificial Intelligence 31, pp. 271–293 (1987); P. V. Fua, “Using probability density functions in the framework of evidential reasoning Uncertainty in knowledge based systems”, B. Bouchon, R. R. Yager, Eds. Springer Verlag (1987); J. J. Chao, E. Drakopoulos, C. C. Lee, “An evidential reasoning approach to distributed multiple hypothesis detection”, Proceedings of the 20th Conference on decision and control, Los Angeles, Calif., December 1987; R. R. Yager, “Entropy and specificity in a mathematical theory of Evidence”, Int J. General Systems, Vol. 9, pp. 249–260 (1983); M. Ishizuka, “Inference methods based on extended Dempster and Shafer's theory for problems with uncertainty/fuzziness”, New Generation Computing 1 (1983), Ohmsha, Ltd, and Springer Verlag p.p. 159–168; L. A. Zadeh, “Fuzzy sets”, Information and Control no. 8, pp. 338–353 (1965); L. A. Zadeh, “Probability measures of fuzzy events”, Journal of Mathematical Analysis and Applications, Vol. 23, pp. 421–427 (1968); A. Kaufmann, “Introduction a la theorie des sous-ensembles flous”, Vol. 1, 2 et 3-Masson-Paris (1975); M. Sugeno, “Theory of fuzzy integrals and its applications”, Tokyo Institute of Technology (1974); R. E. Bellman, L. A. Zadeh, “Decision making in a fuzzy environment”, Management Science, Vol. 17, No. 4, December 1970; D. Dubois, N. Prade, “Fuzzy sets and systems-Theory and applications”, Academic Press, New York (1980); L. A. Zadeh, “Fuzzy sets as a basis for a theory of possibility”, Fuzzy sets and Systems 1, pp. 3–28 (1978); D. Dubois, “Modeles mathematiques de l'imprecis et de l'incertain en vue d'applications aux techniques d'aide a la decision”, Doctoral Thesis, University of Grenoble (1983); D. Dubois, N. Prade, “Theorie des possibilites: application a la representation des connaissances en informatique”, Masson, Paris (1985). Thus, the image or object recognition feature of the present invention may be implemented in the manner of U.S. Pat. No. 5,068,664. Further, it is clear that this recognition feature may form an integral part of certain embodiments of the present invention. It is also clear that the various features of the present invention would be applicable as an adjunct to the various elements of the system disclosed in U.S. Pat. No. 5,068,664.
U.S. Pat. Nos. 5,065,447, and 4,941,193, both incorporated herein by reference, relate to the compression of image data by using fractals. These are discussed in detail below. U.S. Pat. No. 5,065,447 cites a number of references, all incorporated herein by reference, relevant to the use of fractals in image processing: U.S. Pat. No. 4,831,659; “Hidden Variable Fractal Interpolation Functions”, by Barnsley et al., School of Mathematics, Georgia Institute of Technology, Atlanta, Ga. 30332, July, 1986; Barnsley, M. F., and Demko, S., “Iterated Function Systems and The Global Construction of Fractals”, Proc. R. Soc. Lond., A399, 243–275 (1985); Barnsley, M. F., Ervin, V., Hardin, D., Lancaster, J., “Solution of an Inverse Problem for Fractals and Other Sets”, Proc. Natl. Acad. Sci. U.S.A., vol. 83, 1975–1977 (April 1986); “A New Class of Markov Processes for Image Encoding”, School of Mathematics, Georgia Inst. of Technology (1988), pp. 14–32; “Fractal Modelling of Biological Structures”, Perspectives in Biological Dynamics and Theoretical Medicine, Koslow, Mandell, Shlesinger, eds., Annals of New York Academy of Sciences, vol. 504, 179–194 (date unknown); Elton, J., “An Ergodic Theorem for Iterated Maps”, Journal of Ergodic Theory and Dynamical Systems, 7 (1987); “Construction of Fractal Objects with Iterated Function Systems”, Siggraph '85 Proceedings, vol. 19, No. 3, 271–278 (1985); “Fractal Modelling of Real World Images, Lecture Notes for Fractals: Introduction, Basics and Perspectives”, Siggraph (1987); “Packing It In-Fractals . . . ”, Science News, vol. 131, No. 18, pp. 283–285, May 2, 1987; “Fractal Geometry-Understanding Chaos”, Georgia Tech Alumni Magazine, p. 16 (Spring 1986); “Fractals—A Geometry of Nature”, Georgia Institute of Technology Research Horizons, p. 9 (Spring 1986); Fractal Modelling of Biological Structures, School of Mathematics, Georgia Institute of Technology (date unknown); “Packing It In”, Ivars Peterson, Science News, vol. 131, No. 18, pp. 283–285, May 2, 1987; A Better Way to Compress Images, by Barnsley et al., Byte Magazine, January 1988, pp. 213–225; Researchers Use Fractal Geometry, . . . , by Skip Derra, Research and Development Magazine, March 1988; Data Compression: Pntng by Numbrs, The Economist, May 21, 1988; Just the Bare Facts, Please, by William Baldwin, Forbes Magazine, Dec. 12, 1988; “Harnessing Chaos For Images Systhesis”, by Barnsley et al., Computer Graphics, vol. 22, No. 4, August, 1988, pp. 131–140; Chaotic Compression, by Barnsley et al., Computer Graphics World, November 1987; Making a New Science, pp. 215, 239, by James Gleick, date unknown.
Byte Magazine, January 1988., cites, Mandelbrot, B., “The Fractal Geometry of Nature”, W.H. Freeman & Co., San Francisco, Calif., 1982, 1977, and Barnsley, M. F., “Fractals Everywhere”, Academic Press, Boston, Mass., 1988, both of which are also incorporated herein by reference.
U.S. Pat. No. 5,063,603, incorporated herein by reference, relates to a dynamic method for recognizing objects and image processing system therefor. This reference discloses a method of distinguishing between different members of a class of images, such as human beings. A time series of successive relatively high-resolution frames of image data, any frame of which may or may not include a graphical representation of one or more predetermined specific members (e.g., particular known persons) of a given generic class (e.g. human beings), is examined in order to recognize the identity of a specific member; if that member's image is included in the time series. The frames of image data may be examined in real time at various resolutions, starting with a relatively low resolution, to detect whether some earlier-occurring frame includes any of a group of image features possessed by an image of a member of the given class. The image location of a detected image feature is stored and then used in a later-occurring, higher resolution frame to direct the examination only to the image region of the stored location in order to (1) verify the detection of the aforesaid image feature, and (2) detect one or more other of the group of image features, if any is present in that image region of the frame being examined. By repeating this type of examination for later and later occurring frames, the accumulated detected features can first reliably recognize the detected image region to be an image of a generic object of the given class, and later can reliably recognize the detected image region to be an image of a certain specific member of the given class. Thus, the personae recognition feature of the present invention may be implemented in this manner. Further, it is clear that this recognition feature may form an integral part of certain embodiments of the present invention. It is also clear that the various features of the present invention would be applicable as an adjunct to the various elements of the system disclosed in U.S. Pat. No. 5,063,603.
U.S. Pat. No. 5,055,658, incorporated herein by reference, relates to a security system employing digitized personal characteristics, such as voice. The following cited references are incorporated herein by reference: Naik et al., “High Performance Speaker Verification . . . ” ICASSP 86, Tokyo, CH2243-4/86/0000-0881, IEEE 1986, pp. 881–884; “Voice Recognition and Speech Processing”, Elektor Electronics, September 1985, pp. 56–57; Shinan et al., “The Effects of Voice Disguise . . . ” ICASSP 86, Tokyo, CH2243-4/6/0000-0885, IEEE 1986, pp. 885–888. Parts of this system relating to speaker recognition may be used to implement a voice recognition system of the present invention for determining an actor or performer in a broadcast.
U.S. Pat. No. 5,067,164, incorporated herein by reference, relates to a hierarchical constrained automatic learning neural network for character recognition, and thus represents an example of a trainable neural network for pattern recognition, which discloses methods which are useful for the present invention. This patent document cites various references of interest, which are incorporated herein by reference: U.S. Pat. Nos. 4,760,604, 4,774,677 and 4,897,811; D. E. Rumelhart et al., Parallel Distr. Proc.: Explorations in Microstructure of Cognition, vol. 1, 1986, “Learning Internal Representations by Error Propagation”, pp. 318–362; R. P. Lippmann, IEEE ASSP Magazine, vol. 4, No. 2, April 1987, “An Introduction to Computing with Neural Nets”, pp. 4–22; Y. LeCun, Connectionism in Perspective, R. Pfeifer, Z. Schreter, F. Fogelman, L. Steels, (Eds.), 1989, “Generalization and Network Design Strategies”, pp. 143–55; Y. LeCun et al., IEEE Comm. Magazine, November 1989, “Handwritten Digit Recognition: Applications of Neural . . . ”, pp. 41–46. U.S. Pat. Nos. 5,048,100, 5,063,601 and 5,060,278, all incorporated herein by reference, also relate to neural network adaptive pattern recognition methods and apparatuses. It is clear that the methods of U.S. Pat. Nos. 5,048,100, 5,060,278 and 5,063,601 may be used to perform the adaptive pattern recognition functions of the present invention. More general neural networks are disclosed in U.S. Pat. Nos. 5,040,134 and 5,058,184, both incorporated herein be reference, which provide background on the use of neural networks. In particular, U.S. Pat. No. 5,058,184 relates to the use of the apparatus in information processing and feature detection applications.
U.S. Pat. No. 5,058,180, incorporated herein by reference, relates to neural network apparatus and method for pattern recognition, and is thus relevant to the intelligent pattern recognition functions of the present invention. This patent document cites the following documents of interest, which are incorporated herein by reference: U.S. Pat. Nos. 4,876,731 and 4,914,708; Computer Visions, Graphics, and Image Processing 1987, 37, 54–115; L. D. Jackel, H. P. Graf, J. S. Denker, D. Henderson and I. Guyon, “An Application of Neural Net Chips: Handwritten Digit Recognition,” ICNN Proceeding, 1988, pp. II-107–15; G. A. Carpenter and S. Grossberg, “The Art of Adaptive Pattern Recognition by a Self-Organizing Neural Network,” IEEE Computer, March 1988, pp. 77–88; T. F. Pawlicki, D. S. Lee, J. J. Hull and S. N. Srihari, “Neural Network Models and their Application to Handwritten Digit Recognition,” ICNN Proceeding, 1988, pp. II-63–70; E. Gullichsen and E. Chang, “Pattern Classification by Neural Network: An Experiment System for Icon Recognition,” ICNN Proceeding on Neural Networks, March 1987, pp. IV-725–32; S. Grossberg and G. Carpenter, “A Massively Parallel Architecture for a Self-Organizing Neural Pattern Recognition Machine,” Computer Vision, Graphics, and Image Processing (1987, 37, 54–115), pp. 252–315; R. P. Lippman, “An Introduction to Computing with Neural Nets,” IEEE ASSP Magazine, April 1987, pp. 4–22.
U.S. Pat. No. 5,067,161, incorporated herein by reference, relates to a video image pattern recognition system, which recognizes objects in near real time.
U.S. Pat. Nos. 4,817,176 and 4,802,230, both incorporated herein by reference, relate to harmonic transform methods of pattern matching of an undetermined pattern to known patterns, and are useful in the pattern recognition method of the present invention. U.S. Pat. No. 4,998,286, incorporated herein by reference, relates to a harmonic transform method for comparing multidimensional images, such as color images, and is useful in the present pattern recognition methods.
U.S. Pat. No. 5,060,282, incorporated herein by reference, relates to an optical pattern recognition architecture implementing the mean-square error correlation algorithm. This method allows an optical computing function to perform pattern recognition functions. The patent document cites the following references, incorporated herein by reference, which are relevant to optical pattern recognition: D. Psaltis, “Incoherent Electro-Optic Image Correlator”, Optical Engineering, January/February 1984, vol. 23, No. 1, pp. 12–15; P. Kellman, “Time Integrating Optical Signal Processing”, Ph. D. Dissertation, Stanford University, 1979, pp. 51–55; P. Molley, “Implementing the Difference-Squared Error Algorithm Using An Acousto-Optic Processor”, SPIE, vol. 1098, 1989, pp. 232–239; W. Rhodes, “Acousto-Optic Signal Processing: Convolution and Correlation”, Proc. of the IEEE, vol. 69, No. 1, January 1981, pp. 65–79; A. Vander Lugt, “Signal Detection By Complex Spatial Filtering”, IEEE Transactions On Information Theory, IT-10, vol. 2, April 1964, pp. 139–145; D. Psaltis, “Two-Dimensional Optical Processing Using One-Dimensional Input Devices”, Proceedings of the IEEE, vol. 72, No. 7, July 1984, pp. 962–974; P. Molley et al., “A High Dynamic Range Acousto-Optic Image Correlator for Real-Time Pattern Recognition”, SPIE, vol. 938, 1988, pp. 55–65. U.S. Pat. No. 5,063,602, incorporated herein by reference, also relates to an optical image correlators.
U.S. Pat. No. 5,067,160, incorporated herein by reference, relates to a motion-pattern recognition apparatus. The apparatus recognizes a motion of an object which is moving and is hidden in an image signal, and discriminates the object from the background within the signal. The apparatus has an image forming unit comprising non-linear oscillators, which forms an image of the motion of the object in accordance with an adjacent-mutual-interference-rule, on the basis of the image signal. A memory unit, comprising non-linear oscillators, stores conceptualized meanings of several motions. A retrieval unit retrieves a conceptualized meaning close to the motion image of the object. An altering unit alters the rule, on the basis of the conceptualized meaning. The image forming unit, memory unit, retrieval unit and altering unit form a holonic-loop. Successive alterations of the rules by the altering unit within the holonic loop change an ambiguous image formed in the image forming unit into a distinct image. The patent document cites the following references, incorporated herein by reference, which are relevant to the task of discriminating a moving object in a background: U.S. Pat. No. 4,710,964; “Principle of Holonic Computer and Holovision” in Journal of the Institute of Electronics, Information and Communication, vol. 70, No. 9, pp. 921–930 (1987), Shimizu et al; “Holonic Model of Motion Perception”, IEICE Technical Reports, Mar. 26, 1988, pp. 339–346, Omata et al; “Entrainment of Two Coupled van der Pol Oscillators by an External Oscillation”, in Biological Cybernetics, vol. 51, pp. 225–239 (1985), Ohsuga et al. It is clear that U.S. patent discloses an adaptive pattern recognition system that may be useful in various embodiments of the present invention. It is also clear that the interface and control systems of the present invention provide useful adjuncts to the elements disclosed in U.S. Pat. No. 5,067,160.
U.S. Pat. No. 5,065,440, incorporated herein by reference, relates to a pattern recognition apparatus, which compensates for, and is thus insensitive to pattern shifting, thus being useful for decomposing an image into its structural features and recognizing the features. The patent document cites the following references, incorporated herein by reference, which are also relevant to the present invention: U.S. Pat. Nos. 4,543,660, 4,630,308, 4,677,680, 4,809,341, 4,864,629, 4,872,024 and 4,905,296.
U.S. Pat. No. 5,067,166, incorporated herein by reference, relates to a pattern recognition system, in which a local optimum match between subsets of candidate reference label sequences and candidate templates. It is clear that this method is useful in the pattern recognition aspects of the present invention. It is also clear that the interface and control system of the present invention are useful adjuncts to the method disclosed in U.S. Pat. No. 5,067,166.
U.S. Pat. No. 5,048,095, incorporated herein by reference, relates to the use of a genetic learning algorithm to adaptively segment images, which is an initial stage in image recognition. This patent has a software listing for this method. It is clear that this method is useful in the pattern recognition aspects of the present invention. It is also clear that the interface and control system of the present invention are useful adjuncts to the method disclosed in U.S. Pat. No. 5,048,095.
In addition, the following patents are considered relevant to the data compression and pattern recognition functions of the apparatus and interface of the present invention and are incorporated herein by reference: U.S. Pat. Nos. 3,950,733, 4,044,243, 4,254,474, 4,326,259, 4,442,544, 4,449,240, 4,468,704, 4,491,962, 4,501,016, 4,543,660, 4,547,811, 4,630,308, 4,656,665, 4,658,429, 4,660,166, 4,677,680, 4,682,365, 4,685,145, 4,710,822, 4,710,964, 4,719,591, 4,731,863, 4,736,439, 4,742,557, 4,752,890, 4,760,604, 4,764,971, 4,771,467, 4,773,099, 4,774,677, 4,790,025, 4,799,270, 4,803,736, 4,805,224, 4,805,255, 4,809,341, 4,817,171, 4,821,333, 4,823,194, 4,831,659, 4,833,637, 4,837,842, 4,845,610, 4,864,629, 4,872,024, 4,876,731, 4,887,304, 4,888,814, 4,891,762, 4,897,811, 4,905,296, 4,906,099, 4,914,708, 4,926,491, 4,932,065, 4,933,872, 4,941,193, 4,944,023, 4,958,375, 4,958,375, 4,965,725, 4,972,499, 4,979,222, 4,987,604, 4,989,258, 5,014,219, 5,014,327, 5,018,218, 5,018,219, 5,020,112, 5,022,062, 5,034,991, 5,038,379, 5,040,134, 5,046,121, 5,046,122, 5,046,179, 5,048,112, 5,050,223, 5,051,840, 5,052,043, 5,052,045, 5,052,046, 5,053,974, 5,054,093, 5,054,095, 5,054,101, 5,054,103, 5,055,658, 5,055,926, 5,056,147, 5,058,179, 5,058,180, 5,058,186, 5,059,126, 5,060,276, 5,060,277, 5,060,279, 5,060,282, 5,060,285, 5,061,063, 5,063,524, 5,063,525, 5,063,603, 5,063,605, 5,063,608, 5,065,439, 5,065,440, 5,065,447, 5,067,160, 5,067,161, 5,067,162, 5,067,163, 5,067,164, 5,068,664, 5,068,723, 5,068,724, 5,068,744, 5,068,909, 5,068,911, H 331, and Re. 33,316. These patent documents, some of which are mentioned elsewhere in this disclosure, which form a part of this disclosure, may be applied in known manner by those skilled in the art in order to practice various embodiments of the present invention.
The following scientific articles are incorporated by reference, and their relevance is understood by those skilled in the art and relate to the pattern recognition and image compression functions of the apparatus and interface of the present invention:    G. E. Liepins & M. R. Hilliard, “Genetic Algorithms: Foundations & Applications”, Annals of Operations Research, 21, (1989), pp. 31–58.    J. M. Fitzpatrick, J. J. Grefenstette & D. Van Gucht, “Image Registration by Genetic Search”, Conf. Proc., IEEE Southeastcon 1984, pp. 460–464.    A. D. McAulay & J. C. Oh, “Image Learning Classifier System Using Genetic Algorithms”, IEEE Proc. of the National Aerospace & Electronics Conference, vol. 2, 1989, pp. 705–710.    Philip D. Wasserman, “Neural Computing-Theory & Practice”, 1989, pp. 128–129.    N. J. Nilsson, The Mathematical Foundations of Learning Machines ((c) 1990: Morgan Kaufmann Publishers; San Mateo, Calif.) and particularly section 2.6 “The Threshold Logic Unit (TLU)”, on pp. 21–23 and Chapter 6 Layered Machines on pp. 95–114.    G. L. Martin et al., “Recognizing Hand-Printed Letters and Digits Using Backpropagation Learning”, Technical Report of the MCC, Human Interface Laboratory, Austin, Tex., January 1990, pp. 1–9.    J. S. N. Jean et al., “Input Representation and Output Voting Considerations for Handwritten Numeral Recognition with Backpropagation”, International Joint Conference on Neural Networks, Washington, D.C., January 1990, pp. I-408 to I-411.    X. Zhu et al., “Feature Detector and Application to Handwritten Character Recognition”, International Joint Conference on Neural Networks, Washington, D.C., January 1990, pp. II-457 to II-460.    K. Haruki et al., “Pattern Recognition of Handwritten Phonetic Japanese Alphabet Characters”, International Joint Conference on Neural Networks, Washington, D.C., January 1990, pp. II-515 to II-518.    R. K. Miller, Neural Networks ((c) 1989: Fairmont Press; Lilburn, Ga.), pp. 2–12 and Chapter 4 “Implementation of Neural Networks”, on pp. 4–1 to 4–26.    Y. Hayashi et al., “Alphanumeric Character Recognition Using a Connectionist Model with the Pocket Algorithm”, Proceedings of the International Joint Conference on Neural Networks, Washington, D.C. Jun. 18–22, 1989, vol. 2, pp. 606–613.    M. Caudill, “Neural Networks Primer-Part III”, AI Expert, June 1988, pp. 53–59.    D. J. Burr, “A Neural Network Digit Recognizer”, Proceedings of the 1986 IEEE International Conference of Systems, Man and Cybernetics, Atlanta, Ga., pp. 1621–1625.    D. E. Rumelhart, et al., Parallel Distributed Processing, ((c) 1986: MIT Press; Cambridge, Mass.), and specifically Chapter 8 thereof “Learning Internal Representations by Error Propagation”, pp. 318–362.    IEEE Computer, November, 1981, pp. 53–67, “Computer Architectures for Pictorial Inf. Systems”, Per Erik Danielsson et al.    Computing with Neural Circuits: A Model; Science; vol. 233; 8 August 1986; Hopfield et al; pp. 625–633.    Boltzmann Machines: Constraint Satisfaction Networks that Learn; Hinton et al. Tech. Report CMU-CS-85-119; Carnegie-Mellon Univ; May 1984.    Neurons with graded response have collective computational properties like those of two-state neurons; Hopfield; Proc. Natl. Acad. Sci. USA; vol. 81; pp. 3088–3092; May 1984.    Non-Holographic Associative Memory; Nature; vol. 222; pp. 960–962; Jun. 7, 1969; Willshaw et al.    A Possible Organization of Animal Memory and Learning; L. N. Cooper; Nobel 24 (1973); Collective Properties of Physical Systems; pp. 252–264.    Neural Networks and Physical Systems with Emergent Collective Computational Abilities; Hopfield; Proc. Natl. Acad. Sci. USA; vol. 79, April 1982; pp. 2554–25558.    B. G. Batchelor: “Practical Approach to Pattern Classification”, Plenum Press, London and New York; (1974).    B. G. Batchelor: “Pattern Recognition, Ideas in Practice”, Plenum Press, London and New York; (1978).    K. Udagawa et al: “A Parallel Two-Stage Decision Method for Statistical Character Recognition . . . ”, Electronics and Communications in Japan (1965).    J. Schurmann, “Zur Zeichen und Worterkennung beim Automatischen Anschriftenlesen”, Wissenschaftlichl. Berichte, vol. 52, No. 1/2 (1979).    Computers and Biomedical Research 5, 388–410 (1972), pp. 388–410. Proceedings, 6th International Conference on Pattern Recognition 1982, pp. 152–136.    Information Processing 71-North-Holland Publishing Company (1972) pp. 1530–1533.    Scientific American “Not Just a Pretty Face”, March 1990, pp. 77–78.    “Recursive Block Coding—A New Approach to Transform Coding”: IEEE Transactions on Communications, Paul M. Farrelle and Anil K. Jain; vol. Com. 34, No. 2, February 1986.    Yamane et al., “An Image Data Compression Method Using Two-Dimensional Extrapolative Prediction-Discrete Sine Transform”, Oct. 29–31, 1986, pp. 311–316.    Chen et al., “Adaptive Coding of Monochrome and Color Images”, November 1977, pp. 1285–1292.    O'Neal et al., “Coding Isotropic Images”, November 1977, pp. 697–707.    F. Anderson, W. Christiansen, B. Kortegaard, “Real Time, Video Image Centroid Tracker,” Apr. 16–20, 1990.    B. L. Kortegaard, “PAC-MAN, a Precision Alignment Control System for Multiple Laser Beams Self-Adaptive Through the Use of Noise,” Los Alamos National Laboratory, date unknown.    B. L. Kortegaard, “Superfine Laser Position Control Using Statistically Enhanced Resolution in Real Time,” Los Alamos National Laboratory, SPIE-Los Angeles Technical Symposium, Jan. 23–25, 1985.    “Guide to Pattern Recognition Using Random-Access Memories,” I. Aleksander, Computers and Digital Techniques, February 1979, vol. 2, No. 1, pp. 29–40.    D. E. Rumelhart et al., Parallel Distr. Proc.: Explorations in Microstructure of Cognition, vol. 1, 1986, “Learning Internal Representations by Error Propagation”, pp. 318–362.    R. P. Lippmann, IEEE ASSP Magazine, vol. 4, No. 2, April 1987, “An Introduction to Computing with Neural Nets”, pp. 4–22.    Y. LeCun, Connectionism in Perspective, R. Pfeifer, Z. Schreter, F. Fogelman, L. Steels (Eds.), 1989, “Generalization and Network Design Strategies”, pp. 143–155.    Y. LeCun et al., IEEE Comm. Magazine, November 1989, “Handwritten Digit Recognition: Applications of Neural . . . ”, pp. 41–46.    Denker, 1984 International Test Conf., October 1984, Philadelphia, Pa., pp. 558–563.    Gogoussis et al., Proc. SPIE Intl. Soc. Opt. Eng., November 1984, Cambridge, Mass., pp. 121–127.    Svetkoff et al., Hybrid Circuits (GB), No. 13, May 1987, pp. 5–8. Kohonen, Self-Organization & Memory, Second Ed., 1988, Springer-Verlag, pp. 199–209.    Specht, IEEE Internatl. Conf. Neural Networks, vol. 1, July 1988, San Diego, Calif., pp. I-525 to I-532.    Wald, Sequential Analysis, Dover Publications Inc., 1947, pp. 34–43.    Digital Picture Processing, Second Edition, Volume 2, Azriel Rosenfeld and Avinash C. Kak, Academic Press, 1982.    “Towards the construction of a large-scale neural network” (Mori; Electronics Information Communications Association Bulletin PRU 88-59, pp. 87–94).    “Character recognition system using a neural network” (Yamada et. al.: Electronics Information Communications Association Bulletin PRU 88-58, pp. 79–86).    Inspec. Abstract No. 86C010699, Insepc IEE (London) & IEE Coll. on “Adaptive Filters”, Digest No. 76, Oct. 10, 1985, Crawford et al. Adaptive Pattern Recognition Applied To An Expert System For Fault Diagnosis In Telecommunications Equipment, pp. 10/1–8.    Inspec. Abstract No. 84C044315, Inspec IEE (London) & IEE Saraga Colloquium on Electronic Filters, May 21, 1984; Rutter et al., “The Timed Lattice—A New Approach To Fast Converging Equalizer Design”, pp. VIII/1–5.    W. R. Simpson & C. S. Dowling, “WRAPLE: The Weighted Repair Assistance Program Learning Extension”, IEEE Design & Test, vol. No. 2, April 1986; pp. 66–73.    B. B. Dunning, “Self-Learning Data-Base For Automated Fault Localization”, IEEE, 1979; pp. 155–157.    R. M. Stewart, “Expert Systems For Mechanical Fault Diagnosis”, IEEE, 1985; pp. 295–300.    H. K. Lin et al., “Real-Time Screen-Aided Multiple-Image Optical Holographic Matched-Filter Correlator”, Applied Optics, vol. 21, No. 18, Sep. 15, 82, pp. 3278–3286.    A. Vander Lugt et al., “The Use of Film Nonlinearites in Optical Spatial Filtering”, Applied Optics, vol. 9, No. 1, January 1970, pp. 215–222.    A. Vander Lugt, “Practical Considerations for the Use of Spatial Carrier-Frequency Filters”, Applied Optics, vol. 5, No. 11, November 1966, pp. 1760–1765.    Silverston et al., “Spectral Feature Classification and Spatial Pattern Rec.”, SPIE vol. 201 Optical Pattern Recognition (1979), pp. 17–26.    Perry et al., “Auto-Indexing Storage Device”, IBM Tech. Disc. Bulletin, vol. 12, No. 8, January 1970, p. 1219.    Vitols, “Hologram Memory for Storing Digital Data,” IBM Tech. Disc. Bulletin, vol. 8, No. 11, April 1966, pp. 1581–1583.    Stanley R. Sternberg, “Biomedical Image Processing”, 1983, pp. 22–34, IEEE Computer.    “Object Identification and Measurement from Images with Access to the Database to Select Specific Subpopulations of Special Interest”; H. G. Rutherford, F. Taub and B. Williams, May 1986.    Ney, H., et al. “A Data Driven Organization of the Dynamic Programming Beam Search for Continuous Speech Recognition”, Proc. ICASSP 87, pp. 833–836, 1987.    Sakoe, H., “A Generalization of Dynamic Programming Based Pattern Matching Algorithm Stack DP-Matching”, Transactions of the Committee on Speech Research, The Acoustic Society of Japan, p. S83-23, 1983.    Sakoe, H., “A Generalized Two-Level DP-Matching Algorithm for Continuous Speech Recognition”, Transactions of the IECE of Japan, vol. E65, No. 11, pp. 649–656, November 1982.    A. Mahalanobis et al., “Minimum Average Correlation Energy Filters,” Applied Optics, Sep. 1, 1987, vol. 26, No. 17, pp. 3633–40.    R. A. Sprageu, “A Review of Acousto-Optic Signal Correlators,” Optical Engineering, September/October 1977, vol. 16, No. 5, pp. 467–74.    D. Casasent et al., “General I and Q Data Processing on a Multichannel AO System,” Applied Optics, Sep. 15, 1986, vol. 25, No. 18, pp. 3217–24.    Proceedings of the 1988 IEEE National Radar Conference, 20–21 April 1988, pp. 157–164, Vannicola et al., “Applications of Knowledge Based Systems to Surveillance”.    Ksienski et al., “Low Frequency Approach to Target Identification”, Proc. of the IEEE, vol. 63, No. 12, pp. 1651–1660, December 1975.    A. Appriou, “Interet des theories de l'incertain en fusion de donnees”, Colloque International sur le Radar Paris, 24–28 avril 1989.    A. Appriou “Procedure d'aide a la decision multi-informateurs. Applications a la classification multi-capteurs de cibles”, Symposium de l'Avionics Panel (AGARD) Turquie, 25–29 avril 1988.    K. J. Arrow, “Social choice and individual valves”, John Wiley and Sons Inc. (1963).    D. Blair, R. Pollack, “La logique du choix collectif” Pour la Science (1983);    A. Scharlic, “Decider sur plusieurs criteres. Panorama de l'aide a la decision multicritere” Presses Polytechniques Romandes (1985).    R. L. Keeney, B. Raiffa, “Decisions with multiple objectives: Preferences and value tradeoffs”, John Wiley and Sons, New York (1976).    R. J. Jeffrey, “The logic of decision”, The University of Chicago Press, Ltd., London (1983)(2nd Ed.).    B. Roy, “Classements et choix en presence de points de vue multiples”, R.I.R.O.-2eme annee-no. 8, pp. 57–75 (1968).    B. Roy “Electre III: un algorithme de classements fonde sur une representation floue des preferences en presence de criteres multiples” Cahiers du CERO, Vol. 20, no. 1, pp. 3–24 (1978),    R. O. Duda, P. E. Hart, M. J. Nilsson, “Subjective Bayesian methods for rule-based inference systems”, Technical Note 124-Artificial Intelligence Center-SRI International.    R. K. Bhatnagar, L. N. Kamal, “Handling uncertain information: a review of numeric and non-numeric methods”, Uncertainty in Artificial Intelligence, L. N. Kamal and J. F. Lemmer, Eds. (1986).    A. P. Dempster, “Upper and lower probabilities induced by a multivalued mapping”, Annals of mathematical Statistics, no. 38 (1967).    A. P. Dempster, “A generalization of Bayesian inference”, Journal of the Royal Statistical Society, Vol. 30, Series B (1968).    G. Shafer, “A mathematical theory of evidence”, Princeton University Press, Princeton, N.J. (1976).    D. Dubois, N. Prade, “Combination of uncertainty with belief functions: a reexamination”, Proceedings 9th International Joint Conference on Artificial Intelligence, Los Angeles (1985).    H. E. Kyburg, “Bayesian and non Bayesian evidential updating”, Artificial Intelligence 31, pp. 271–293 (1987).    P. V. Fua, “Using probability density functions in the framework of evidential reasoning Uncertainty in knowledge based systems”, B. Bouchon, R. R. Yager, Eds. Springer Verlag (1987).    J. J. Chao, E. Drakopoulos, C. C. Lee, “An evidential reasoning approach to distributed multiple hypothesis detection”, Proceedings of the 20th Conference on decision and control, Los Angeles, Calif., December 1987.    R. R. Yager, “Entropy and specificity in a mathematical theory of Evidence”, Int J. General Systems, Vol. 9, pp. 249–260 (1983).    M. Ishizuka, “Inference methods based on extended Dempster and Shafer's theory for problems with uncertainty/fuzziness”, New Generation Computing 1 (1983), Ohmsha, Ltd, and Springer Verlag p.p. 159–168.    L. A. Zadeh, “Fuzzy sets”, Information and Control no. 8, pp. 338–353 (1965); L. A. Zadeh, “Probability measures of fuzzy events”, Journal of Mathematical Analysis and Applications, Vol. 23, pp. 421–427 (1968).    A. Kaufmann, “Introduction a la theorie des sous-ensembles flous”, Vol. 1, 2 et 3-Masson-Paris (1975).    M. Sugeno, “Theory of fuzzy integrals and its applications”, Tokyo Institute of Technology (1974).    R. E. Bellman, L. A. Zadeh, “Decision making in a fuzzy environment”, Management Science, Vol. 17, No. 4, December 1970.    D. Dubois, N. Prade, “Fuzzy sets and systems-Theory and applications”, Academic Press, New York (1980).    L. A. Zadeh, “Fuzzy sets as a basis for a theory of possibility”, Fuzzy sets and Systems 1, pp. 3–28 (1978).    D. Dubois, “Modeles mathematiques de l'imprecis et de l'incertain en vue d'applications aux techniques d'aide a la decision”, Doctoral Thesis, University of Grenoble (1983).    D. Dubois, N. Prade, “Theorie des possibilites: application a la representation des connaissances en informatique”, Masson, Paris (1985).    “Hidden Variable Fractal Interpolation Functions”, by Barnsley et al., School of Mathematics, Georgia Institute of Technology, Atlanta, Ga. 30332, July, 1986.    L. Anson, M. Barnsley, “Graphics Compression Technology”, SunWorld, October 1991, pp. 43–52.    “Fractal Compression Breakthrough for Multimedia Applications”, B. Caffery, Inside, Oct. 9, 1991.    Fractal Modelling of Real World Images, Lecture Notes for Fractals: Introduction, Basics and Perspectives, Siggraph (1987).    Fractal Geometry—Understanding Chaos, Georgia Tech Alumni Magazine, p. 16 (Spring 1986).    “Fractals Yield High Compression”, Electronic Engineering Times, Sep. 30, 1991, p. 39.    Fractals—A Geometry of Nature, Georgia Institute of Technology Research Horizons, p. 9 (Spring 1986).    Fractal Modelling of Biological Structures, School of Mathematics, Georgia Institute of Technology (date unknown).    Packing It In by Ivars Peterson, Science News, vol. 131, No. 18, p. 283, May 2, 1987.    A Better Way to Compress Images, by Barnsley et al., Byte Magazine, January 1988.    Harnessing Chaos For Images Systhesis, by Barnsley et al., Computer Graphics, vol. 22, No. 4, August 1988.    Naik et al., “High Performance Speaker Verification ICASSP 86, Tokyo, CH2243-4/86/0000-0881, IEEE 1986, pp. 881–884.    “Voice Recognition and Speech Processing”, Elektor Electronics, September 1985, pp. 56–57.    Shinan et al., “The Effects of Voice Disguise . . . ” ICASSP 86, Tokyo, CH2243-4/86/0000-0885, IEEE 1986, pp. 885–888.    Computer Visions, Graphics, and Image Processing 1987, 37, 54–115.    L. D. Jackel, H. P. Graf, J. S. Denker, D. Henderson and I. Guyon, “An Application of Neural Net Chips: Handwritten Digit Recognition,” ICNN Proceeding, 1988, pp. II-107–15.    G. A. Carpenter and S. Grossberg, “The Art of Adaptive Pattern Recognition by a Self-Organizing Neural Network,” IEEE Computer, March 1988, pp. 77–88.    T. F. Pawlicki, D. S. Lee, J. J. Hull and S. N. Srihari, “Neural Network Models and their Application to Handwritten Digit Recognition,” ICNN Proceeding, 1988, pp. II-63–70.    E. Gullichsen and E. Chang, “Pattern Classification by Neural Network: An Experiment System for Icon Recognition,” ICNN Proceeding on Neural Networks, March 1987, pp. IV-725–32.    S. Grossberg and G. Carpenter, “A Massively Parallel Architecture for a Self-Organizing Neural Pattern Recognition Machine,” Computer Vision, Graphics, and Image Processing (1987, 37, 54–115), pp. 252–315.    R. P. Lippman, “An Introduction to Computing with Neural Nets,” IEEE ASSP Magazine, April 1987, pp. 4–22.    D. Psaltis, “Incoherent Electro-Optic Image Correlator”, Optical Engineering, January/February 1984, vol. 23, No. 1, pp. 12–15.    P. Kellman, “Time Integrating Optical Signal Processing”, Ph. D. Dissertation, Stanford University, 1979, pp. 51–55.    P. Molley, “Implementing the Difference-Squared Error Algorithm Using An Acousto-Optic Processor”, SPIE, vol. 1098, 1989, pp. 232–239.    W. Rhodes, “Acousto-Optic Signal Processing: Convolution and Correlation”, Proc. of the IEEE, vol. 69, No. 1, January 1981, pp. 65–79.    A. Vander Lugt, “Signal Detection By Complex Spatial Filtering”, IEEE Transactions On Information Theory, IT-10, vol. 2, April 1964, pp. 139–145.    D. Psaltis, “Two-Dimensional Optical Processing Using One-Dimensional Input Devices”, Proceedings of the IEEE, vol. 72, No. 7, July 1984, pp. 962–974.    P. Molley et al., “A High Dynamic Range Acousto-Optic Image Correlator for Real-Time Pattern Recognition”, SPIE, vol. 938, 1988, pp. 55–65.    “Principle of Holonic Computer and Holovision” in Journal of the Institute of Electronics, Information and Communication, vol. 70, No. 9, pp. 921–930 (1987), Shimizu et al.    “Holonic Model of Motion Perception”, IEICE Technical Reports, Mar. 26, 1988, pp. 339–346, Omata et al.    “Entrainment of Two Coupled van der Pol Oscillators by an External Oscillation”, in Biological Cybernetics, vol. 51, pp. 225–239 (1985), Ohsuga et al.
The above-mentioned references are exemplary, and are not meant to be limiting in respect to the resources available to those skilled in the art. Of course it should be realized that the hardware available and the choice of specific method or software algorithm are interactive, and therefore must be specified together, however, it is noted that in view of the present disclosure, it is obvious to combine compatible technologies to achieve the advanced interface and control system of the present invention.